Despite its unique capabilities, diffusion-weighted imaging (DWI) in prostate is inherently limited by low signal-to-noise ratio (SNR). Currently, gains in SNR of high b-value images are achieved through increase in the number of excitations (NEX), at the cost of increase in total acquisition time. We demonstrate feasibility of improving prostate DWI image quality by leveraging denoising convolutional network. Using pairs of "noisy" NEX4 and "clean" NEX16 DWI images, reconstructed from raw data, CNN was trained to denoise prostate DWI images. Denoising of images significantly improved SNR and increased overall image quality, reviewed by two experienced genitourinary radiologists.